Affiliations
Department of Biostatistics and Bioinformatics, Duke University Medical
Center, Durham, North Carolina, United States of America,
Center for Human Genetics, Duke University Medical Center, Durham, North
Carolina, United States of America

Affiliations
Duke University Eye Center, Duke University Medical Center, Durham, North
Carolina, United States of America,
Department of Pathology, Duke University Medical Center, Durham, North
Carolina, United States of America

Figures

Abstract

Fuchs endothelial corneal dystrophy (FECD) is a common, late-onset disorder of
the corneal endothelium. Although progress has been made in understanding the
genetic basis of FECD by studying large families in which the phenotype is
transmitted in an autosomal dominant fashion, a recently reported genome-wide
association study identified common alleles at a locus on chromosome 18 near
TCF4 which confer susceptibility to FECD. Here, we report
the findings of our independent validation study for TCF4 using
the largest FECD dataset to date (450 FECD cases and 340 normal controls).
Logistic regression with sex as a covariate was performed for three genetic
models: dominant (DOM), additive (ADD), and recessive (REC). We found
significant association with rs613872, the target marker reported by Baratz
et al.(2010), for all three genetic models (DOM:
P = 9.33×10−35;
ADD:
P = 7.48×10−30;
REC:
P = 5.27×10−6).
To strengthen the association study, we also conducted a genome-wide linkage
scan on 64 multiplex families, composed primarily of affected sibling pairs
(ASPs), using both parametric and non-parametric two-point and multipoint
analyses. The most significant linkage region localizes to chromosome 18 from
69.94cM to 85.29cM, with a peak multipoint
HLOD = 2.5 at rs1145315 (75.58cM) under the DOM
model, mapping 1.5 Mb proximal to rs613872. In summary, our study presents
evidence to support the role of the intronic TCF4 single
nucleotide polymorphism rs613872 in late-onset FECD through both association and
linkage studies.

Funding: Funding for this work was provided by NEI R01 grants EY016514 (GKK), EY016835
(JDG and NK), EY015543 (RRA), EY019126 (MAH) and EY013315 (MAH); and Research to
Prevent Blindness (NAA; http://www.rpbusa.org/rpb/). NK is a Distinguished Brumley
Professor. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Fuchs endothelial corneal dystrophy (FECD), first described by Ernst Fuchs [1], is a
common progressive disorder of the corneal endothelium that typically becomes
symptomatic during the fifth or sixth decade of life [2], [3], although corneal
endothelial abnormalities can be clinically detected several years before patients
become symptomatic [4]. The disorder affects as much as 4% of
the United States population over the age of 40 years [5]–[7] and
occurs predominantly in women, who comprise approximately 75% of cases
[8].
This debilitating disorder leads to corneal edema with a loss of corneal clarity,
painful episodes of recurrent corneal erosions, severe impairment of visual acuity,
and sometimes even blindness.

FECD is often inherited as an autosomal dominant trait [5], [9]–[13] and 50% of
clinical cases of FECD are estimated to show familial clustering [2]. While
it is recognized that FECD has a genetic basis, few genes have been identified that
explain the genetic underpinnings of FECD susceptibility. The first gene to be
causally linked with FECD was COL8A2 (MIM: 120252) on chromosome 1,
in which two missense mutations, p.L450W and p.Q455V/Q455K, have been replicated in
rare early-onset (before 40 years of age) FECD multigenerational families [3], [13]–[15] and in atypical sporadic
FECD cases [15]. Additional COL8A2 missense
mutations have been identified in late-onset FECD patients, although their role
remains unclear due to conflicting results [16]–[18].
Mutations in two additional genes that play roles in other corneal endothelial
dystrophies have also been suggested to cause FECD. SLC4A11 (MIM:
610206), which causes autosomal recessive congenital hereditary endothelial
dystrophy (CHED2, MIM: 217700) [19], was found to contain mutations in Chinese,
Indian, and Caucasian patients with the common late-onset phenotype of FECD [20], [21].
Additionally, mutations in TCF8 (MIM: 609141), whose loss of
function causes posterior polymorphous corneal dystrophy (PPCD, MIM: 609141) [22], were
recently reported in late-onset Caucasian [23] but not in Chinese
FECD patients [24].

Several genome-wide linkage scans using large multigenerational families have
reported FECD loci on chromosomes 13 (FCD1), 18
(FCD2), 5 (FCD3), and 9
(FCD4) [23], [25]–[27]. Our own genome-wide
linkage scan using affected sibpairs (ASPs) from 21 late-onset FECD families and one
large multigenerational family [28] detected five regions of linkage with multipoint
LOD scores >1.5, including the chromosome 1p region near
COL8A2. Neither our late-onset FECD families nor our large,
multigenerational family replicated the four previous FECD loci
(FCD1 to FCD4), but the number of families
used in the study may have been underpowered to detect significant linkage.

The first genome-wide association study (GWAS) for FECD was reported recently, using
an initial dataset of 130 unrelated cases and 260 unaffected controls genotyped with
the Illumina 370K BeadChip panel [29]. After genotyping their most significant findings
in a replication dataset containing 150 cases and 150 controls, Baratz and
colleagues concluded that a single nucleotide polymorphism (SNP) on chromosome
18q21, rs613872, in an intron of a gene encoding transcription factor 4
(TCF4, MIM: 602272) showed genome-wide significant association
with FECD
(P = 1.10x10−12
for the initial GWAS dataset;
P = 1.79x10−13
for the replication dataset;
P = 2.34x10−26
for the combined dataset).

Here we present additional evidence for the presence of a FECD locus on chromosome
18. We report the results from an expansion of our previous linkage study, as well
as an association study analyzing the association of rs613872 with FECD in a dataset
containing 450 unrelated FECD cases and 340 unaffected controls, the largest sample
of FECD patients interrogated to date.

Materials and Methods

Ethics Statement

Our study was performed in accordance with the Declaration of Helsinki and the
institutional review boards at Duke University Medical Center and Johns Hopkins
University (JHU) specifically approved this study. Both FECD study sites, the
Cornea Clinics at Duke University Eye Center (DUEC) and the Wilmer Eye Institute
at JHU, obtained the appropriate institutional review board approval for
research on human subjects prior to initiating subject recruitment, and all
individuals gave written, informed consent. The control subjects from the Duke
glaucoma genetics study were also recruited under the approval of the Duke
Institutional Review Board, and consented to allow their biological samples to
be used by other research studies.

Two Caucasian datasets were used for association and linkage studies. The
association dataset consisted of 450 unrelated FECD cases, each with a Krachmer
grade ≥2, and 340 unaffected controls with age at enrollment of
≥45 years old. The 450 unrelated cases used here were either probands
from the family dataset (see below) or singleton cases, all of whom were
ascertained through the cornea clinic at DUEC. The unrelated controls were from
26 unaffected married-in spouses from the DUEC family dataset and 314 control
subjects from the glaucoma genetic study at DUEC [30]. Glaucoma control
subjects underwent detailed eye examination and had no signs of corneal
abnormalities at the time of subject enrollment.

The linkage dataset consisted of 64 multiplex families (at least two affected
individuals per family) containing a total of 215 subjects (69.8%
females). Families were ascertained independently from the Cornea Clinics at
DUEC or at the Wilmer Eye Institute at JHU. Demographic data, including age at
enrollment and gender, for the individuals analyzed in the association and
linkage datasets are summarized in Table 1.

Marker Selection and Genotyping

Association analysis focused on the SNP rs613872 in TFC4, the
most significantly associated SNP in the GWAS performed by Baratz and colleagues
[29]. We also genotyped rs10490775 in
PTPRG on chromosome 3, which was significant but not at a
genome-wide level in the Baratz GWAS, to examine possible replication in our
dataset. The two SNPs were genotyped with pre-designed TaqMan® allelic
discrimination assays (Life Technologies, formerly Applied Biosystems, Inc.,
Foster City, CA), which use unlabeled polymerase chain reaction (PCR) primers
and two allele-specific probes containing the TaqMan® minor groove
binding group (MGB) probe and either a FAM™ and VIC® dye label
in a 384-well plate format. PCR reactions were performed with Taqman®
Universal PCR Master Mix on the GeneAmp® PCR System 9700 (Applied
Biosystems, Inc.), and the ABI7900HT Fast PCR System (Applied Biosystems, Inc.)
was used for reading allelic discrimination calls. Quality control (QC) samples,
including two CEPH (Centre d'Etude du Polymorphisme Humain) pedigree
individuals, one no-template sample, and two duplicate samples (one male, one
female), were contained within each quadrant of each 384-well plate. These QC
samples were used to provide duplicate samples within one quadrant, across
quadrants within one plate, and across plates. Results of the CEPH and QC sample
genotyping were compared to identify possible sample plating errors and
genotype-calling inconsistencies, and none were observed. A threshold of
95% genotyping efficiency was required for submission to the analysis
database.

Two Illumina (San Diego, CA) SNP linkage panels were used for genome-wide linkage
marker genotyping. Seventy-two of the 215 linkage samples were included in our
previous linkage study [28], and were genotyped using the Illumina
GoldenGate linkage panel IVB, which contains 5,858 SNPs. The remaining 143
samples were genotyped using the Illumina Infinium HumanLinkage-12 platform
containing 6,090 SNPs, of which 4,811 overlap with the GoldenGate IVB platform.
All genomic DNA samples were prepared at a concentration of 75 ng/µL,
in a total volume of 10 µL, and were genotyped in four multiplexed
assays according to the manufacturer's protocols. All DNA samples were
extracted from blood using the PureGene system (Gentra Systems, Minneapolis,
MN).

Data cleaning for the association dataset

Reproducibility of genotypes was examined for the replicate QC samples located in
each 384-well Taqman plate to assess genotyping quality. Reproducibility rates
of 100% were observed. The two SNP markers, rs613872 and rs10490775,
were examined for possible deviations from Hardy-Weinberg equilibrium (HWE).

Data cleaning for the linkage dataset

We instituted several QC measures to determine the final set of markers for
analysis. We first used the Illumina BeadStudio program to check genotype
reproducibility rates using the replicated samples, family relationships, gender
status of each DNA sample using Y chromosome markers, sample genotype call rates
>99%, and the GenCall score which is a quality measure for
each genotype used in the Illumina genotyping system. In particular, the GenCall
score measures how close a genotype is to the center of the cluster of other
samples assigned the same genotype, as compared with the centers of the clusters
of the other genotypes, and ranges from 0 to 1. The higher the GenCall score the
more reliable the genotype. A set of clean markers was chosen based on GenCall
scores ≥0.15, as well as meeting the other QC measures described
previously, for further data cleaning.

For additional data quality assurance, 1000 markers with approximately equal
inter-marker distances across the genome were selected to examine family
relationships using the RELPAIR [31] and PREST [32]
programs. After correcting for family relationship errors, Mendelian inheritance
inconsistencies were checked using the PEDCHECK program [33]. Missing
genotypes were assigned to the family for those markers with Mendelian
inheritance inconsistencies.

All SNPs were tested for deviations from HWE. We randomly chose one affected
individual per family to include in an unrelated affected dataset, and one
unaffected individual per family was selected to include in an unrelated,
unaffected dataset. An exact test implemented in the Genetic Data Analysis (GDA)
program was used to test HWE, by performing 3,200 permutations to estimate the
empirical p-value for each marker [34]. We applied a
Bonferroni correction (significance threshold
= 0.05/total number of markers) to determine
significant deviation from HWE. Markers that were out of HWE in the unaffected
dataset were excluded in the linkage analysis.

Association Analysis

Genotypes of rs613872 (T and G nucleotide polymorphism) were tested using three
genetic models: dominant (DOM), additive (ADD), and recessive (REC). The
genotypes of (GG, TG, and TT) were coded as (1, 1, 0) for DOM; (2, 1, 0) for
ADD; and (1, 0, 0) for REC where allele G is the rare allele. A logistic
regression association analysis in PLINK [35] was applied with
gender as a covariate. We also stratified our association dataset by gender and
tested for association of this marker within the males and females. A nominal
significance level of 0.05 was applied to declare significant association. The
relationship between disease severity (grade level) and genotypes of rs613872
was examined using Fisher Exact test, because some genotype-phenotype has small
number of samples, particularly for grade 4.

Linkage analysis

Two-point parametric linkage analyses using the FASTLINK [36] and HOMOG [37]
programs (http://linkage.rockefeller.edu/, provided in the public domain
by Rockefeller University, New York, NY) were performed to generate
heterogeneity logarithm of the odds (HLOD) scores. Since the mode of inheritance
is unknown for FECD, both DOM and REC genetic models were assumed in the
parametric analysis with disease allele frequencies of 0.001 and 0.01
respectively. The penetrance (chance of an individual being affected if carrying
the disease susceptibility genotype) was based on the assumed genetic models,
and was consistent with the goal of conducting an affecteds-only parametric
linkage analysis.

MERLIN [38] was used to perform both multipoint
parametric and non-parametric linkage analyses (NPL). The same DOM and REC
models described above were assumed in the MERLIN parametric analysis. It is
known that linkage disequilibrium (LD) may inflate the type I error of a
multipoint linkage analysis, particularly when parental genotype data are not
available—as is the case for most of our families [39]. We
set a threshold of the squared Pearson correlation coefficient (r2)
between markers at 0.16 in MERLIN to ensure independence between markers for
multipoint linkage analysis.

Results

Association results

We tested rs613872 in our case-control association dataset of 450 cases and 340
controls; Table
1 summarizes the demographic data of our subjects. We did not
observe deviation from HWE in the control group for rs613872
(P = 0.60), indicating high
quality genotyping results. However, we found strong deviation from HWE for this
marker in the FECD case group (P<0.001). This may be the
source of the highly significant association signal we observed in our dataset
for all three genetic models (DOM:
P = 9.33×10−35;
ADD:
P = 7.48×10−30;
REC:
P = 5.27×10−6).
The effect size of the minor (risk) allele (G) in our dataset was strong: (1)
DOM: odds ratio (OR) = 8.01, confidence
interval (CI) of OR = (5.80; 11.29); (2) ADD
model: OR = 5.35,
CI = (4.00; 7.14); (3) REC:
OR = 4.06,
CI = (2.22; 7.42). These results are
consistent with the findings of Baratz and colleagues [29]. Table 2 shows
the comparison of genotype frequencies between our FECD cases and controls to
the genotype frequencies observed in the FECD GWAS cases and controls studied by
Baratz and colleagues. Clearly, the FECD case group has excessive heterozygous
genotypes for rs613872 compared to the controls in both datasets, which is
consistent to the observation of deviation from HWE in FECD cases. As for
rs10490775 in PTPRG on chromosome 3, we did not detect
significant association with FECD (DOM
P = 0.98; ADD
P = 0.96; REC
P = 0.92).

When we examined gender-specific groups, we observed the same elevation of the
heterozygous (GT) genotype in cases relative to controls in both genders (Table 2). The SNP rs613872
is still significant in both genders (male
P = 3×10−26;
female
P = 1.2×10−16).
However, this result may not reflect the true level of association due to the
small sample size, particularly for males. Furthermore, the results between
genders should not be compared due to the unbalanced sample sizes. Among our 450
cases, we have 235 cases with grading information. We did not find the
correlation between disease severity and genotypes of rs613872
(P = 0.13). However, more samples may be needed
to make a solid conclusion.

Linkage scan samples and markers

After implementing QC measures for the linkage dataset we removed five
individuals from three families due to relationship errors that could not be
resolved and five individuals due to low sample call rates
(<95%). In total, we analyzed 215 individuals in 64 families
(Table
1), including 41 ASPs from 64 families, making this the largest
dataset used in a genome-wide linkage scan for FECD to date. We noted a high
proportion of females in both our linkage and association datasets, likely
because of the known gender imbalance among FECD patients [8].

A total of 7,291 markers were genotyped between the two SNP genotyping platforms,
with 4,551 markers overlapping between the two chips. Of those, 4,533 markers
met our QC criteria for linkage analyses, which included having a GenCall score
≥0.15, a genotype call frequency ≥95%, and no
significant deviation from HWE. Eleven markers produced Mendelian inheritance
inconsistencies within three families and were assigned missing genotypes within
those families. In the multipoint analysis using MERLIN, 3,927 markers met our
LD criteria and were analyzed.

Linkage regions

Eighteen SNPs on ten chromosomes gave two-point HLOD scores ≥2 in either
the DOM or REC models (Table 3). In particular, three markers produced
HLOD scores above 3: rs1889974 (chromosome 10, 119.34 cM,
HLOD = 3.37) and rs235512 (chromosome 15, 61.47
cM, HLOD = 3.53) under the DOM model and
rs893186 (chromosome 19, 110.25 cM,
HLOD = 3.56) under the REC model. Among all
markers listed in Table
3, only rs998876 on chromosome 5 (194.22 cM) overlaps with
a previously identified FECD linkage locus, FCD3[27]. Table 4 summarizes the eight
linkage regions that produced peak multipoint LOD scores ≥1.5 in any of
the three multipoint linkage analyses, the nonparametric (NPL) and parametric
(DOM and REC) analyses. The boundaries of a linkage region outlined in Table 4 are
based on the chromosomal interval that covers one LOD score below the peak
marker's LOD score. Two separate regions of linkage were found on
chromosomes 16 (28.27–36.15 cM and 56.82–96.27 cM) and X
(59.18–83.83 cM and 98.35–124.34 cM), implying that multiple
genes on the same chromosome could be influencing FECD.

The most promising multipoint linkage region was on chromosome 18 from 69.94 cM
to 85.29 cM with a peak multipoint HLOD = 2.5
at rs1145315 (75.58 cM) under the DOM model. The nonparametric analysis showed
consistent results for this region with a peak multipoint
HLOD = 1.48 at the same peak marker (Table 4). In
addition, rs4941043 within this same interval produced a HLOD
score = 1.99 under the two-point DOM model
(Table
3). This multipoint linkage peak on chromosome 18 overlaps with
the FCD2 peak reported by Sundin et al.[26]
and with the most significant GWAS hit, rs613872 in TCF4,
reported by Baratz et al.[29]
(Figure 1).
The SNP rs613872 in TCF4 is only 1.4 Mb away from our peak DOM
multipoint marker, rs1145315.

SNP markers are plotted along the x-axis by their deCODE map position,
and the LOD/HLOD scores for each marker are plotted along the y-axis.
The results of the FASTLINK/HOMOG dominant two-point analysis are
indicated with black circles, and the results of the MERLIN dominant
multipoint analysis are indicated with a black line. The SNP rs4941043
is the peak marker from the two-point analysis (see Table
3). The location of the FCD2 peak (Sundin
et al., 2006) and the most significantly associated
SNP, rs613872, from the FECD GWAS performed by Baratz et
al. (2010) are indicated by arrows. The location of the
TCF4 gene is also indicated for reference. 2PT,
two-point results; MPT, multipoint results; cM, centiMorgans; LOD,
logarithm of the odds; HLOD, heterogeneity logarithm of the odds.

Discussion

To date, progress towards identifying the genetic underpinnings of FECD has been
limited to a handful of genes, including COL8A2,
SLC4A11, and TCF8[3],
[13],
[20],
[21], [23] that have arisen
from candidate gene or genome-wide linkage studies. Linkage scans have additionally
identified four genetic loci (FCD1 to FCD4) that
appear to influence familial FECD [23], [25]–[27]. In spite of these
insights, knowledge of the genetic basis of non-familial FECD has remained limited.
Recently, Baratz and colleagues identified significant statistical association
between a SNP in TCF4 and FECD in the first genome-wide association
study carried out for FECD [29]. Their study identified an intronic SNP in
TCF4, rs613872, with highly significant allelic and genotypic
p-values of 2.34×10−26 and
1.29×10−18, respectively, in their combined
analysis of their small discovery and replication datasets.

Here, we present a large case-control association dataset that replicates these
recent genome-wide association data [29], revealing a highly significant association
between rs613872 and FECD with a p-value of
9.33×10−35 (DOM). Additionally, we show that the
best multipoint linkage region from our family dataset is also located on chromosome
18 (68.94 cM to 85.29 cM), with a peak multipoint
HLOD = 2.5 at rs1145315, 1.5 Mb away from rs613872
in TCF4. The consistent findings in both our linkage and
association studies for an association of rs613872 with FECD together with the
findings of Baratz et al. suggest that the association is probably
not spurious but, rather, is due to certain recombination events in this region that
may increase susceptibility to FECD. Additionally, the excess of heterozygous
genotypes at rs613872 in both our cases and those of Baratz et al.
warrants further investigation. Although we replicated the association of rs613872
with FECD in our dataset, we failed to replicate the association with rs10490775 in
PTPRG. Since we also did not detect evidence of linkage to
chromosome 3, we hypothesize that either this locus does not influence FECD risk or
its effect on FECD risk is small.

It is of great interest to know if rs613872 genotypes can predict disease severity.
Our 235 cases with grading information did not show significant correlation to the
genotypes of rs613872, which is consistent to the finding from Riazuddin et al.
[40], where 180 cases were used. Although both studies
obtained the same observation, the limited sample sizes are the drawback for making
a solid conclusion. Many of our early samples were given disease status diagnosis
rather than detailed grading information. As our recruitment of FECD cases moving
forward, we will be able to increase the number of cases with grading. The disease
severity vs. genotypes of any target marker will be able to be evaluated
properly.

According to data from the Human Genome Diversity Project, the minor (risk) allele,
G, of rs613872 is rare to nonexistent in populations from Africa, Eastern Asia, and
Central and South America, yet is more frequent in European, Middle Eastern, and
Southern Asian populations (Figure 2, [41]). Given that the FECD
cases analyzed by our group and by Baratz et al. are of European
descent, it will be important to examine whether the association between rs613872
and FECD risk replicates in other ethnic and racial populations. It is possible that
alternative TCF4 risk alleles may be associated in other ethnic and
racial populations, which would argue for this locus being functionally important in
FECD pathogenesis. Alternatively, if the true FECD disease variant is in LD with
rs613872, a lack of association in other ethnic and racial populations may help
explain why the disorder has a lower prevalence in other populations like African,
East Asian, and South American populations.

Data from the Human Genome Diversity Project, available online through the
UCSC genome browser at http://genome.ucsc.edu/. Note
the higher prevalence of the risk allele in sample populations from Europe,
the Middle East, and Southern Asia and the absence of the risk allele in
sample populations from Africa, Eastern Asia, and Central and South
America.

GWAS studies commonly raise the question of how to interpret the biological
significance of statistically associated SNPs, which may be located in intronic or
intergenic regions with no obvious connection to the disease phenotype. Baratz and
colleagues did not find any coding variations within the TCF4 gene
that might help explain the functional mechanism behind the association of rs613872
with FECD, so clearly further studies are needed to detect such variations if they
exist. Although TCF4 is an attractive candidate gene within the
FCD2 locus, much remains unknown. If TCF4
plays a role in familial FECD, further research is needed todiscover the mutation(s)
within TCF4 that segregate with disease in families that show
evidence of linkage to FCD2. l. Until such a causative mutation(s)
is identified, restraint needs to be exercised in not drawing premature conclusions
that a causal link between the TCF4 protein and FECD has been identified.

Expression data have indicated that TCF4 is expressed in eye
tissues, particularly in the corneal endothelium [29], [42]–[46],
However, Tcf4 mutant mice do not appear to contain vision/eye
abnormalities as a phenotypic feature [47], [48]. Additionally, ENCODE data on the UCSC genome
browser [49] (March 2006 assembly) shows that rs613872 is
contained within the chromatin immunoprecipitation sequence (ChIP-seq)-purported
binding site for two transcription factors, Ini1
(SMARCB1) and Brg1 (SMARCA4).
These are both components of the SWI/SNF chromatin remodeling complex that is
required for transcriptional regulation of genes normally repressed by chromatin
[50],
[51].
Given that TCF4 is a transcription factor, it is possible that
variation at this site might have an effect on the spatiotemporal expression of
TCF4 and, in turn, affect the expression of some of its
targets. Further studies are required to investigate the veracity this
hypothesis.

Our study provides supporting evidence of a linkage signal to the strong association
results of rs613872. This not only further confirms the importance of a locus on
chromosome 18 in influencing FECD risk, but also indicates that a causal variant for
FECD may be identified within this locus. It is highly possible that rs613872 is
tagging a rare variant within this locus that plays a role in the etiology of FECD.
Additional sequencing studies using family datasets that are highly linked to this
locus is a potential strategy to identify the biological mechanisms underlying how
this locus influences FECD pathogenesis.

Acknowledgments

The authors would like to thank the patients and their family members who generously
agreed to participate in these studies, and the Corneal Dystrophy Foundation.

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